Overview

Dataset statistics

Number of variables35
Number of observations2149
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory587.7 KiB
Average record size in memory280.1 B

Variable types

Numeric16
Categorical19

Alerts

DoctorInCharge has constant value "XXXConfid"Constant
HeadInjury is highly imbalanced (55.5%)Imbalance
PatientID is uniformly distributedUniform
PatientID has unique valuesUnique
BMI has unique valuesUnique
AlcoholConsumption has unique valuesUnique
PhysicalActivity has unique valuesUnique
DietQuality has unique valuesUnique
SleepQuality has unique valuesUnique
CholesterolTotal has unique valuesUnique
CholesterolLDL has unique valuesUnique
CholesterolHDL has unique valuesUnique
CholesterolTriglycerides has unique valuesUnique
MMSE has unique valuesUnique
FunctionalAssessment has unique valuesUnique
ADL has unique valuesUnique

Reproduction

Analysis started2025-11-29 16:29:57.304740
Analysis finished2025-11-29 16:30:22.169418
Duration24.86 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

PatientID
Real number (ℝ)

Uniform  Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5825
Minimum4751
Maximum6899
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2025-11-29T16:30:22.238753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4751
5-th percentile4858.4
Q15288
median5825
Q36362
95-th percentile6791.6
Maximum6899
Range2148
Interquartile range (IQR)1074

Descriptive statistics

Standard deviation620.50719
Coefficient of variation (CV)0.10652484
Kurtosis-1.2
Mean5825
Median Absolute Deviation (MAD)537
Skewness0
Sum12517925
Variance385029.17
MonotonicityStrictly increasing
2025-11-29T16:30:22.343458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47511
 
< 0.1%
47521
 
< 0.1%
47531
 
< 0.1%
47541
 
< 0.1%
47551
 
< 0.1%
47561
 
< 0.1%
47571
 
< 0.1%
47581
 
< 0.1%
47591
 
< 0.1%
47601
 
< 0.1%
Other values (2139)2139
99.5%
ValueCountFrequency (%)
47511
< 0.1%
47521
< 0.1%
47531
< 0.1%
47541
< 0.1%
47551
< 0.1%
47561
< 0.1%
47571
< 0.1%
47581
< 0.1%
47591
< 0.1%
47601
< 0.1%
ValueCountFrequency (%)
68991
< 0.1%
68981
< 0.1%
68971
< 0.1%
68961
< 0.1%
68951
< 0.1%
68941
< 0.1%
68931
< 0.1%
68921
< 0.1%
68911
< 0.1%
68901
< 0.1%

Age
Real number (ℝ)

Distinct31
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.908795
Minimum60
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2025-11-29T16:30:22.434929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile61
Q167
median75
Q383
95-th percentile89
Maximum90
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.9902214
Coefficient of variation (CV)0.12001557
Kurtosis-1.189214
Mean74.908795
Median Absolute Deviation (MAD)8
Skewness0.045964341
Sum160979
Variance80.82408
MonotonicityNot monotonic
2025-11-29T16:30:22.528950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
6884
 
3.9%
8884
 
3.9%
7282
 
3.8%
7681
 
3.8%
7180
 
3.7%
9079
 
3.7%
6777
 
3.6%
6074
 
3.4%
7074
 
3.4%
6673
 
3.4%
Other values (21)1361
63.3%
ValueCountFrequency (%)
6074
3.4%
6168
3.2%
6270
3.3%
6369
3.2%
6459
2.7%
6564
3.0%
6673
3.4%
6777
3.6%
6884
3.9%
6963
2.9%
ValueCountFrequency (%)
9079
3.7%
8972
3.4%
8884
3.9%
8768
3.2%
8650
2.3%
8557
2.7%
8471
3.3%
8371
3.3%
8268
3.2%
8157
2.7%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
1
1088 
0
1061 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
11088
50.6%
01061
49.4%

Length

2025-11-29T16:30:22.631358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T16:30:22.693109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
11088
50.6%
01061
49.4%

Most occurring characters

ValueCountFrequency (%)
11088
50.6%
01061
49.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2149
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11088
50.6%
01061
49.4%

Most occurring scripts

ValueCountFrequency (%)
Common2149
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11088
50.6%
01061
49.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11088
50.6%
01061
49.4%

Ethnicity
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1278 
1
454 
3
211 
2
206 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row3
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01278
59.5%
1454
 
21.1%
3211
 
9.8%
2206
 
9.6%

Length

2025-11-29T16:30:22.770436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T16:30:22.834855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01278
59.5%
1454
 
21.1%
3211
 
9.8%
2206
 
9.6%

Most occurring characters

ValueCountFrequency (%)
01278
59.5%
1454
 
21.1%
3211
 
9.8%
2206
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2149
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01278
59.5%
1454
 
21.1%
3211
 
9.8%
2206
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
Common2149
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01278
59.5%
1454
 
21.1%
3211
 
9.8%
2206
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01278
59.5%
1454
 
21.1%
3211
 
9.8%
2206
 
9.6%

EducationLevel
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
1
854 
2
636 
0
446 
3
213 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1854
39.7%
2636
29.6%
0446
20.8%
3213
 
9.9%

Length

2025-11-29T16:30:22.915286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T16:30:22.977637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1854
39.7%
2636
29.6%
0446
20.8%
3213
 
9.9%

Most occurring characters

ValueCountFrequency (%)
1854
39.7%
2636
29.6%
0446
20.8%
3213
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2149
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1854
39.7%
2636
29.6%
0446
20.8%
3213
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
Common2149
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1854
39.7%
2636
29.6%
0446
20.8%
3213
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1854
39.7%
2636
29.6%
0446
20.8%
3213
 
9.9%

BMI
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.655697
Minimum15.008851
Maximum39.992767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2025-11-29T16:30:23.064937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15.008851
5-th percentile16.349202
Q121.611408
median27.823924
Q333.869778
95-th percentile38.856789
Maximum39.992767
Range24.983916
Interquartile range (IQR)12.25837

Descriptive statistics

Standard deviation7.2174381
Coefficient of variation (CV)0.26097473
Kurtosis-1.184508
Mean27.655697
Median Absolute Deviation (MAD)6.1650578
Skewness-0.026714591
Sum59432.093
Variance52.091413
MonotonicityNot monotonic
2025-11-29T16:30:23.167171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.927749231
 
< 0.1%
26.827681191
 
< 0.1%
17.795882441
 
< 0.1%
33.800817041
 
< 0.1%
20.716973831
 
< 0.1%
30.626885551
 
< 0.1%
38.387621861
 
< 0.1%
18.776009411
 
< 0.1%
27.833188381
 
< 0.1%
35.456301731
 
< 0.1%
Other values (2139)2139
99.5%
ValueCountFrequency (%)
15.008851181
< 0.1%
15.01207071
< 0.1%
15.014659191
< 0.1%
15.018239931
< 0.1%
15.031271341
< 0.1%
15.035723521
< 0.1%
15.036742871
< 0.1%
15.070944461
< 0.1%
15.083201431
< 0.1%
15.085792851
< 0.1%
ValueCountFrequency (%)
39.992767461
< 0.1%
39.988512831
< 0.1%
39.981532631
< 0.1%
39.964860741
< 0.1%
39.94632131
< 0.1%
39.934174531
< 0.1%
39.915136161
< 0.1%
39.890864651
< 0.1%
39.886912321
< 0.1%
39.837742461
< 0.1%

Smoking
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1529 
1
620 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
01529
71.1%
1620
28.9%

Length

2025-11-29T16:30:23.257901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T16:30:23.311958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01529
71.1%
1620
28.9%

Most occurring characters

ValueCountFrequency (%)
01529
71.1%
1620
28.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2149
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01529
71.1%
1620
28.9%

Most occurring scripts

ValueCountFrequency (%)
Common2149
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01529
71.1%
1620
28.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01529
71.1%
1620
28.9%

AlcoholConsumption
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.039442
Minimum0.0020030991
Maximum19.989293
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2025-11-29T16:30:23.384155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0020030991
5-th percentile1.0175833
Q15.1398096
median9.9344124
Q315.157931
95-th percentile19.03159
Maximum19.989293
Range19.98729
Interquartile range (IQR)10.018121

Descriptive statistics

Standard deviation5.7579103
Coefficient of variation (CV)0.57352893
Kurtosis-1.2028757
Mean10.039442
Median Absolute Deviation (MAD)5.0126197
Skewness0.018414567
Sum21574.76
Variance33.153531
MonotonicityNot monotonic
2025-11-29T16:30:23.483188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.297217731
 
< 0.1%
4.5425238181
 
< 0.1%
19.555084531
 
< 0.1%
12.209265551
 
< 0.1%
18.454356091
 
< 0.1%
4.1401437841
 
< 0.1%
0.64604727051
 
< 0.1%
13.723825711
 
< 0.1%
12.167847631
 
< 0.1%
16.028688241
 
< 0.1%
Other values (2139)2139
99.5%
ValueCountFrequency (%)
0.0020030991361
< 0.1%
0.010504438791
< 0.1%
0.0187377281
< 0.1%
0.036260467071
< 0.1%
0.042764979591
< 0.1%
0.06528599681
< 0.1%
0.079313528871
< 0.1%
0.10346000681
< 0.1%
0.11881736671
< 0.1%
0.12710975631
< 0.1%
ValueCountFrequency (%)
19.989293361
< 0.1%
19.988291321
< 0.1%
19.985621511
< 0.1%
19.984018421
< 0.1%
19.974442571
< 0.1%
19.97292711
< 0.1%
19.966875141
< 0.1%
19.966670361
< 0.1%
19.960887561
< 0.1%
19.954861091
< 0.1%

PhysicalActivity
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9202021
Minimum0.0036160168
Maximum9.9874294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2025-11-29T16:30:23.583256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0036160168
5-th percentile0.4692145
Q12.5706265
median4.7664243
Q37.4278988
95-th percentile9.4586127
Maximum9.9874294
Range9.9838134
Interquartile range (IQR)4.8572724

Descriptive statistics

Standard deviation2.8571911
Coefficient of variation (CV)0.58070604
Kurtosis-1.1791965
Mean4.9202021
Median Absolute Deviation (MAD)2.4131042
Skewness0.044972594
Sum10573.514
Variance8.1635411
MonotonicityNot monotonic
2025-11-29T16:30:23.683964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.3271124741
 
< 0.1%
7.619884541
 
< 0.1%
7.8449877911
 
< 0.1%
8.428001351
 
< 0.1%
6.3104606891
 
< 0.1%
0.21106163071
 
< 0.1%
9.257694911
 
< 0.1%
4.6494506681
 
< 0.1%
1.5313597881
 
< 0.1%
6.4407726871
 
< 0.1%
Other values (2139)2139
99.5%
ValueCountFrequency (%)
0.0036160168261
< 0.1%
0.0074825923581
< 0.1%
0.0093476615321
< 0.1%
0.019956905851
< 0.1%
0.022461559751
< 0.1%
0.031857294521
< 0.1%
0.045405487251
< 0.1%
0.050560277761
< 0.1%
0.065493809911
< 0.1%
0.068155471751
< 0.1%
ValueCountFrequency (%)
9.9874294131
< 0.1%
9.9865539271
< 0.1%
9.9850688481
< 0.1%
9.9840895411
< 0.1%
9.9839938171
< 0.1%
9.9765814541
< 0.1%
9.974595191
< 0.1%
9.9612485291
< 0.1%
9.9553160191
< 0.1%
9.9479095871
< 0.1%

DietQuality
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9931379
Minimum0.0093847201
Maximum9.9983457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2025-11-29T16:30:23.787450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0093847201
5-th percentile0.44044086
Q12.4584549
median5.0760873
Q37.5586247
95-th percentile9.4818056
Maximum9.9983457
Range9.988961
Interquartile range (IQR)5.1001698

Descriptive statistics

Standard deviation2.909055
Coefficient of variation (CV)0.58261058
Kurtosis-1.2289618
Mean4.9931379
Median Absolute Deviation (MAD)2.541761
Skewness-0.01205775
Sum10730.253
Variance8.462601
MonotonicityNot monotonic
2025-11-29T16:30:23.890552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3472143061
 
< 0.1%
0.51876713871
 
< 0.1%
1.8263346651
 
< 0.1%
7.435604141
 
< 0.1%
0.79549750891
 
< 0.1%
1.5849220111
 
< 0.1%
5.8973879271
 
< 0.1%
8.3419031921
 
< 0.1%
6.7368820441
 
< 0.1%
8.0860191211
 
< 0.1%
Other values (2139)2139
99.5%
ValueCountFrequency (%)
0.0093847201161
< 0.1%
0.012645731621
< 0.1%
0.013055683761
< 0.1%
0.014332337871
< 0.1%
0.016445695151
< 0.1%
0.019939764041
< 0.1%
0.024542579941
< 0.1%
0.025412343521
< 0.1%
0.028973736291
< 0.1%
0.032104685391
< 0.1%
ValueCountFrequency (%)
9.9983456791
< 0.1%
9.9972029241
< 0.1%
9.9802812031
< 0.1%
9.9712041351
< 0.1%
9.971091291
< 0.1%
9.9680273071
< 0.1%
9.9627817071
< 0.1%
9.9561992491
< 0.1%
9.9548793421
< 0.1%
9.9524509621
< 0.1%

SleepQuality
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0510811
Minimum4.0026287
Maximum9.9998403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2025-11-29T16:30:23.992540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.0026287
5-th percentile4.2608327
Q15.4829972
median7.1156462
Q38.5625209
95-th percentile9.7302183
Maximum9.9998403
Range5.9972117
Interquartile range (IQR)3.0795237

Descriptive statistics

Standard deviation1.7635729
Coefficient of variation (CV)0.25011384
Kurtosis-1.2124536
Mean7.0510811
Median Absolute Deviation (MAD)1.5407098
Skewness-0.069630253
Sum15152.773
Variance3.1101895
MonotonicityNot monotonic
2025-11-29T16:30:24.101528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.0256786661
 
< 0.1%
7.1512927431
 
< 0.1%
9.6735741581
 
< 0.1%
8.3925536851
 
< 0.1%
5.5972376781
 
< 0.1%
7.2619525051
 
< 0.1%
5.4776855941
 
< 0.1%
4.2132099251
 
< 0.1%
5.7482238691
 
< 0.1%
7.5517734441
 
< 0.1%
Other values (2139)2139
99.5%
ValueCountFrequency (%)
4.002628661
< 0.1%
4.0041733531
< 0.1%
4.006170921
< 0.1%
4.0083881791
< 0.1%
4.0086852221
< 0.1%
4.0115378811
< 0.1%
4.0135793971
< 0.1%
4.0160667821
< 0.1%
4.0202114581
< 0.1%
4.0228860361
< 0.1%
ValueCountFrequency (%)
9.9998403171
< 0.1%
9.9992012961
< 0.1%
9.9976272651
< 0.1%
9.9940791491
< 0.1%
9.9930388571
< 0.1%
9.9893990941
< 0.1%
9.9892280611
< 0.1%
9.9885876691
< 0.1%
9.9884516171
< 0.1%
9.9863177161
< 0.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1607 
1
542 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01607
74.8%
1542
 
25.2%

Length

2025-11-29T16:30:24.199122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T16:30:24.251587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01607
74.8%
1542
 
25.2%

Most occurring characters

ValueCountFrequency (%)
01607
74.8%
1542
 
25.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2149
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01607
74.8%
1542
 
25.2%

Most occurring scripts

ValueCountFrequency (%)
Common2149
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01607
74.8%
1542
 
25.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01607
74.8%
1542
 
25.2%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1839 
1
310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01839
85.6%
1310
 
14.4%

Length

2025-11-29T16:30:24.316369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T16:30:24.369674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01839
85.6%
1310
 
14.4%

Most occurring characters

ValueCountFrequency (%)
01839
85.6%
1310
 
14.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2149
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01839
85.6%
1310
 
14.4%

Most occurring scripts

ValueCountFrequency (%)
Common2149
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01839
85.6%
1310
 
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01839
85.6%
1310
 
14.4%

Diabetes
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1825 
1
324 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01825
84.9%
1324
 
15.1%

Length

2025-11-29T16:30:24.436401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T16:30:24.484938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01825
84.9%
1324
 
15.1%

Most occurring characters

ValueCountFrequency (%)
01825
84.9%
1324
 
15.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2149
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01825
84.9%
1324
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
Common2149
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01825
84.9%
1324
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01825
84.9%
1324
 
15.1%

Depression
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1718 
1
431 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01718
79.9%
1431
 
20.1%

Length

2025-11-29T16:30:24.549343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T16:30:24.606156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01718
79.9%
1431
 
20.1%

Most occurring characters

ValueCountFrequency (%)
01718
79.9%
1431
 
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2149
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01718
79.9%
1431
 
20.1%

Most occurring scripts

ValueCountFrequency (%)
Common2149
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01718
79.9%
1431
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01718
79.9%
1431
 
20.1%

HeadInjury
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1950 
1
199 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01950
90.7%
1199
 
9.3%

Length

2025-11-29T16:30:24.670367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T16:30:24.723708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01950
90.7%
1199
 
9.3%

Most occurring characters

ValueCountFrequency (%)
01950
90.7%
1199
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2149
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01950
90.7%
1199
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
Common2149
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01950
90.7%
1199
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01950
90.7%
1199
 
9.3%

Hypertension
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1829 
1
320 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01829
85.1%
1320
 
14.9%

Length

2025-11-29T16:30:24.793672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T16:30:24.851384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01829
85.1%
1320
 
14.9%

Most occurring characters

ValueCountFrequency (%)
01829
85.1%
1320
 
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2149
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01829
85.1%
1320
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common2149
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01829
85.1%
1320
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01829
85.1%
1320
 
14.9%

SystolicBP
Real number (ℝ)

Distinct90
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.26477
Minimum90
Maximum179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2025-11-29T16:30:24.936347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile94
Q1112
median134
Q3157
95-th percentile175
Maximum179
Range89
Interquartile range (IQR)45

Descriptive statistics

Standard deviation25.949352
Coefficient of variation (CV)0.19326999
Kurtosis-1.1975928
Mean134.26477
Median Absolute Deviation (MAD)23
Skewness0.0099710423
Sum288535
Variance673.36887
MonotonicityNot monotonic
2025-11-29T16:30:25.036252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15537
 
1.7%
10634
 
1.6%
12634
 
1.6%
16033
 
1.5%
16533
 
1.5%
13033
 
1.5%
10732
 
1.5%
17832
 
1.5%
11731
 
1.4%
12431
 
1.4%
Other values (80)1819
84.6%
ValueCountFrequency (%)
9027
1.3%
9126
1.2%
9221
1.0%
9321
1.0%
9426
1.2%
9525
1.2%
9621
1.0%
9724
1.1%
9824
1.1%
9923
1.1%
ValueCountFrequency (%)
17924
1.1%
17832
1.5%
17723
1.1%
17622
1.0%
17523
1.1%
17421
1.0%
17311
 
0.5%
17230
1.4%
17126
1.2%
17019
0.9%

DiastolicBP
Real number (ℝ)

Distinct60
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.847836
Minimum60
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2025-11-29T16:30:25.130640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile62
Q174
median91
Q3105
95-th percentile116
Maximum119
Range59
Interquartile range (IQR)31

Descriptive statistics

Standard deviation17.592496
Coefficient of variation (CV)0.19580323
Kurtosis-1.2350404
Mean89.847836
Median Absolute Deviation (MAD)15
Skewness-0.054469941
Sum193083
Variance309.49592
MonotonicityNot monotonic
2025-11-29T16:30:25.229816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6153
 
2.5%
6247
 
2.2%
11646
 
2.1%
10246
 
2.1%
10345
 
2.1%
6444
 
2.0%
7143
 
2.0%
10043
 
2.0%
10742
 
2.0%
8741
 
1.9%
Other values (50)1699
79.1%
ValueCountFrequency (%)
6026
1.2%
6153
2.5%
6247
2.2%
6335
1.6%
6444
2.0%
6536
1.7%
6625
1.2%
6734
1.6%
6828
1.3%
6938
1.8%
ValueCountFrequency (%)
11938
1.8%
11835
1.6%
11733
1.5%
11646
2.1%
11535
1.6%
11436
1.7%
11336
1.7%
11238
1.8%
11138
1.8%
11034
1.6%

CholesterolTotal
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225.19752
Minimum150.09332
Maximum299.99335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2025-11-29T16:30:25.479391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum150.09332
5-th percentile157.36182
Q1190.25296
median225.08643
Q3262.03166
95-th percentile291.5952
Maximum299.99335
Range149.90004
Interquartile range (IQR)71.778693

Descriptive statistics

Standard deviation42.542233
Coefficient of variation (CV)0.18891075
Kurtosis-1.156042
Mean225.19752
Median Absolute Deviation (MAD)35.905273
Skewness-0.018674284
Sum483949.47
Variance1809.8416
MonotonicityNot monotonic
2025-11-29T16:30:25.589858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
242.36683971
 
< 0.1%
231.1625951
 
< 0.1%
284.18185781
 
< 0.1%
159.58223961
 
< 0.1%
237.60218361
 
< 0.1%
280.71253871
 
< 0.1%
263.7341491
 
< 0.1%
151.38313681
 
< 0.1%
233.60575521
 
< 0.1%
281.63005021
 
< 0.1%
Other values (2139)2139
99.5%
ValueCountFrequency (%)
150.09331561
< 0.1%
150.13557161
< 0.1%
150.19218331
< 0.1%
150.21264991
< 0.1%
150.28701411
< 0.1%
150.40304921
< 0.1%
150.4449451
< 0.1%
150.45964891
< 0.1%
150.57569551
< 0.1%
150.75399041
< 0.1%
ValueCountFrequency (%)
299.99335251
< 0.1%
299.95999141
< 0.1%
299.89013351
< 0.1%
299.87325871
< 0.1%
299.86848251
< 0.1%
299.6599261
< 0.1%
299.63814591
< 0.1%
299.50607421
< 0.1%
299.32671441
< 0.1%
299.30746731
< 0.1%

CholesterolLDL
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.33594
Minimum50.230707
Maximum199.96567
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2025-11-29T16:30:25.684632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50.230707
5-th percentile57.443444
Q187.195798
median123.34259
Q3161.73373
95-th percentile192.80735
Maximum199.96567
Range149.73496
Interquartile range (IQR)74.537935

Descriptive statistics

Standard deviation43.366584
Coefficient of variation (CV)0.34878558
Kurtosis-1.2083206
Mean124.33594
Median Absolute Deviation (MAD)37.132961
Skewness0.036233409
Sum267197.94
Variance1880.6606
MonotonicityNot monotonic
2025-11-29T16:30:25.792457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.150896961
 
< 0.1%
193.40799551
 
< 0.1%
153.32276221
 
< 0.1%
65.366636841
 
< 0.1%
92.869699881
 
< 0.1%
198.33462851
 
< 0.1%
52.470669631
 
< 0.1%
69.623510411
 
< 0.1%
144.04573961
 
< 0.1%
130.49758041
 
< 0.1%
Other values (2139)2139
99.5%
ValueCountFrequency (%)
50.230706561
< 0.1%
50.282301791
< 0.1%
50.400002961
< 0.1%
50.430083051
< 0.1%
50.466968761
< 0.1%
50.480669691
< 0.1%
50.707076841
< 0.1%
50.793086551
< 0.1%
50.891910081
< 0.1%
51.097577851
< 0.1%
ValueCountFrequency (%)
199.96566511
< 0.1%
199.93656521
< 0.1%
199.80717921
< 0.1%
199.66812841
< 0.1%
199.46172141
< 0.1%
199.437851
< 0.1%
199.42047021
< 0.1%
199.36798891
< 0.1%
199.35898721
< 0.1%
199.24960671
< 0.1%

CholesterolHDL
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.463533
Minimum20.003434
Maximum99.980324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2025-11-29T16:30:25.894988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20.003434
5-th percentile24.487935
Q139.095698
median59.768237
Q378.93905
95-th percentile96.22624
Maximum99.980324
Range79.97689
Interquartile range (IQR)39.843351

Descriptive statistics

Standard deviation23.139174
Coefficient of variation (CV)0.38913217
Kurtosis-1.2178539
Mean59.463533
Median Absolute Deviation (MAD)19.940067
Skewness0.042205687
Sum127787.13
Variance535.42137
MonotonicityNot monotonic
2025-11-29T16:30:26.019465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.68256351
 
< 0.1%
79.028477321
 
< 0.1%
69.772291861
 
< 0.1%
68.457490711
 
< 0.1%
56.874304671
 
< 0.1%
79.080503291
 
< 0.1%
66.533369481
 
< 0.1%
77.346816481
 
< 0.1%
43.075893171
 
< 0.1%
74.291247291
 
< 0.1%
Other values (2139)2139
99.5%
ValueCountFrequency (%)
20.003434011
< 0.1%
20.015124831
< 0.1%
20.064239971
< 0.1%
20.263950791
< 0.1%
20.366770741
< 0.1%
20.422355151
< 0.1%
20.577161781
< 0.1%
20.689966741
< 0.1%
20.725533221
< 0.1%
20.742605451
< 0.1%
ValueCountFrequency (%)
99.980324081
< 0.1%
99.959494251
< 0.1%
99.958358031
< 0.1%
99.932496471
< 0.1%
99.836900271
< 0.1%
99.809436751
< 0.1%
99.770308161
< 0.1%
99.768954781
< 0.1%
99.742077021
< 0.1%
99.682816381
< 0.1%

CholesterolTriglycerides
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228.2815
Minimum50.407194
Maximum399.94186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2025-11-29T16:30:26.138134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50.407194
5-th percentile67.986071
Q1137.58322
median230.30198
Q3314.83905
95-th percentile382.99946
Maximum399.94186
Range349.53467
Interquartile range (IQR)177.25582

Descriptive statistics

Standard deviation101.98672
Coefficient of variation (CV)0.4467586
Kurtosis-1.2190259
Mean228.2815
Median Absolute Deviation (MAD)88.247884
Skewness-0.032923232
Sum490576.94
Variance10401.291
MonotonicityNot monotonic
2025-11-29T16:30:26.239959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
162.18914311
 
< 0.1%
294.63090921
 
< 0.1%
83.638324141
 
< 0.1%
277.57735751
 
< 0.1%
291.19878021
 
< 0.1%
263.94365491
 
< 0.1%
216.48917491
 
< 0.1%
210.57086611
 
< 0.1%
151.16418621
 
< 0.1%
144.17597451
 
< 0.1%
Other values (2139)2139
99.5%
ValueCountFrequency (%)
50.407193621
< 0.1%
50.461610691
< 0.1%
50.775339751
< 0.1%
50.973755511
< 0.1%
50.992717141
< 0.1%
51.064226941
< 0.1%
51.241951021
< 0.1%
51.472924111
< 0.1%
51.97939071
< 0.1%
52.052096621
< 0.1%
ValueCountFrequency (%)
399.94186161
< 0.1%
399.88813761
< 0.1%
399.85432171
< 0.1%
399.79176221
< 0.1%
399.72969761
< 0.1%
399.68431091
< 0.1%
399.23971081
< 0.1%
398.91074351
< 0.1%
398.85578531
< 0.1%
398.82319311
< 0.1%

MMSE
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.755132
Minimum0.0053121464
Maximum29.991381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2025-11-29T16:30:26.340054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0053121464
5-th percentile1.5199773
Q17.1676023
median14.44166
Q322.161028
95-th percentile28.27137
Maximum29.991381
Range29.986068
Interquartile range (IQR)14.993426

Descriptive statistics

Standard deviation8.6131513
Coefficient of variation (CV)0.58373936
Kurtosis-1.2303648
Mean14.755132
Median Absolute Deviation (MAD)7.5007356
Skewness0.03238178
Sum31708.779
Variance74.186375
MonotonicityNot monotonic
2025-11-29T16:30:26.434404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.463532361
 
< 0.1%
20.613267311
 
< 0.1%
7.3562486251
 
< 0.1%
13.991127241
 
< 0.1%
13.51760891
 
< 0.1%
27.517528981
 
< 0.1%
1.9644126911
 
< 0.1%
10.139568431
 
< 0.1%
25.820731871
 
< 0.1%
28.388409421
 
< 0.1%
Other values (2139)2139
99.5%
ValueCountFrequency (%)
0.0053121464421
< 0.1%
0.01802171121
< 0.1%
0.035301041481
< 0.1%
0.046927529351
< 0.1%
0.047936786831
< 0.1%
0.050623512471
< 0.1%
0.12705125261
< 0.1%
0.13689566171
< 0.1%
0.14866087971
< 0.1%
0.15511933111
< 0.1%
ValueCountFrequency (%)
29.991380561
< 0.1%
29.974262121
< 0.1%
29.95942481
< 0.1%
29.950813131
< 0.1%
29.926298771
< 0.1%
29.880098511
< 0.1%
29.839425371
< 0.1%
29.833992831
< 0.1%
29.825271671
< 0.1%
29.795692771
< 0.1%

FunctionalAssessment
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.080055
Minimum0.0004595936
Maximum9.9964671
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2025-11-29T16:30:26.539474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0004595936
5-th percentile0.48263118
Q12.5662809
median5.0944387
Q37.5469813
95-th percentile9.5538819
Maximum9.9964671
Range9.9960075
Interquartile range (IQR)4.9807004

Descriptive statistics

Standard deviation2.8927435
Coefficient of variation (CV)0.56943153
Kurtosis-1.1830512
Mean5.080055
Median Absolute Deviation (MAD)2.5058758
Skewness-0.034576218
Sum10917.038
Variance8.3679648
MonotonicityNot monotonic
2025-11-29T16:30:26.646186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.5188769731
 
< 0.1%
7.1186955041
 
< 0.1%
5.8950773451
 
< 0.1%
8.9651063041
 
< 0.1%
6.0450387741
 
< 0.1%
5.5101440751
 
< 0.1%
6.0621244741
 
< 0.1%
3.4013735071
 
< 0.1%
7.396060981
 
< 0.1%
1.148903531
 
< 0.1%
Other values (2139)2139
99.5%
ValueCountFrequency (%)
0.00045959359581
< 0.1%
0.011898315581
< 0.1%
0.013210891311
< 0.1%
0.015187268451
< 0.1%
0.020428089961
< 0.1%
0.036685705931
< 0.1%
0.044254483011
< 0.1%
0.04497070421
< 0.1%
0.044987974221
< 0.1%
0.053668706191
< 0.1%
ValueCountFrequency (%)
9.9964670731
< 0.1%
9.9926095791
< 0.1%
9.9913567861
< 0.1%
9.9900568381
< 0.1%
9.9864407041
< 0.1%
9.9864103131
< 0.1%
9.9758333371
< 0.1%
9.9736969891
< 0.1%
9.9646707421
< 0.1%
9.9484506481
< 0.1%

MemoryComplaints
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1702 
1
447 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01702
79.2%
1447
 
20.8%

Length

2025-11-29T16:30:26.744826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T16:30:26.799133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01702
79.2%
1447
 
20.8%

Most occurring characters

ValueCountFrequency (%)
01702
79.2%
1447
 
20.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2149
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01702
79.2%
1447
 
20.8%

Most occurring scripts

ValueCountFrequency (%)
Common2149
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01702
79.2%
1447
 
20.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01702
79.2%
1447
 
20.8%

BehavioralProblems
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1812 
1
337 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
01812
84.3%
1337
 
15.7%

Length

2025-11-29T16:30:26.864404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T16:30:26.917252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01812
84.3%
1337
 
15.7%

Most occurring characters

ValueCountFrequency (%)
01812
84.3%
1337
 
15.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2149
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01812
84.3%
1337
 
15.7%

Most occurring scripts

ValueCountFrequency (%)
Common2149
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01812
84.3%
1337
 
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01812
84.3%
1337
 
15.7%

ADL
Real number (ℝ)

Unique 

Distinct2149
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9829584
Minimum0.0012879277
Maximum9.9997471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.9 KiB
2025-11-29T16:30:26.995974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0012879277
5-th percentile0.39259084
Q12.3428363
median5.0389727
Q37.5814901
95-th percentile9.4719418
Maximum9.9997471
Range9.9984592
Interquartile range (IQR)5.2386538

Descriptive statistics

Standard deviation2.9497748
Coefficient of variation (CV)0.59197259
Kurtosis-1.2495009
Mean4.9829584
Median Absolute Deviation (MAD)2.6135182
Skewness-0.030435804
Sum10708.378
Variance8.7011714
MonotonicityNot monotonic
2025-11-29T16:30:27.103122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.725883461
 
< 0.1%
2.5924241331
 
< 0.1%
7.1195477431
 
< 0.1%
6.4812258591
 
< 0.1%
0.014691221291
 
< 0.1%
9.0156862821
 
< 0.1%
9.2363282751
 
< 0.1%
4.5172482731
 
< 0.1%
0.75623180761
 
< 0.1%
4.5543938651
 
< 0.1%
Other values (2139)2139
99.5%
ValueCountFrequency (%)
0.0012879277021
< 0.1%
0.0043543628271
< 0.1%
0.009273646911
< 0.1%
0.014691221291
< 0.1%
0.015305827031
< 0.1%
0.022626240361
< 0.1%
0.022999160561
< 0.1%
0.03153464971
< 0.1%
0.03559074461
< 0.1%
0.041136517341
< 0.1%
ValueCountFrequency (%)
9.9997471221
< 0.1%
9.9881585711
< 0.1%
9.9726630151
< 0.1%
9.962793721
< 0.1%
9.9474404631
< 0.1%
9.945036381
< 0.1%
9.9438051671
< 0.1%
9.9406310311
< 0.1%
9.9347898791
< 0.1%
9.9287405661
< 0.1%

Confusion
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1708 
1
441 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01708
79.5%
1441
 
20.5%

Length

2025-11-29T16:30:27.191972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T16:30:27.244788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01708
79.5%
1441
 
20.5%

Most occurring characters

ValueCountFrequency (%)
01708
79.5%
1441
 
20.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2149
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01708
79.5%
1441
 
20.5%

Most occurring scripts

ValueCountFrequency (%)
Common2149
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01708
79.5%
1441
 
20.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01708
79.5%
1441
 
20.5%

Disorientation
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1809 
1
340 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01809
84.2%
1340
 
15.8%

Length

2025-11-29T16:30:27.305905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T16:30:27.358613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01809
84.2%
1340
 
15.8%

Most occurring characters

ValueCountFrequency (%)
01809
84.2%
1340
 
15.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2149
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01809
84.2%
1340
 
15.8%

Most occurring scripts

ValueCountFrequency (%)
Common2149
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01809
84.2%
1340
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01809
84.2%
1340
 
15.8%

PersonalityChanges
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1825 
1
324 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
01825
84.9%
1324
 
15.1%

Length

2025-11-29T16:30:27.422417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T16:30:27.473011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01825
84.9%
1324
 
15.1%

Most occurring characters

ValueCountFrequency (%)
01825
84.9%
1324
 
15.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2149
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01825
84.9%
1324
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
Common2149
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01825
84.9%
1324
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01825
84.9%
1324
 
15.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1808 
1
341 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
01808
84.1%
1341
 
15.9%

Length

2025-11-29T16:30:27.532983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T16:30:27.580046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01808
84.1%
1341
 
15.9%

Most occurring characters

ValueCountFrequency (%)
01808
84.1%
1341
 
15.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2149
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01808
84.1%
1341
 
15.9%

Most occurring scripts

ValueCountFrequency (%)
Common2149
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01808
84.1%
1341
 
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01808
84.1%
1341
 
15.9%

Forgetfulness
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1501 
1
648 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01501
69.8%
1648
30.2%

Length

2025-11-29T16:30:27.647370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T16:30:27.693092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01501
69.8%
1648
30.2%

Most occurring characters

ValueCountFrequency (%)
01501
69.8%
1648
30.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2149
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01501
69.8%
1648
30.2%

Most occurring scripts

ValueCountFrequency (%)
Common2149
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01501
69.8%
1648
30.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01501
69.8%
1648
30.2%

Diagnosis
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
0
1389 
1
760 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2149
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01389
64.6%
1760
35.4%

Length

2025-11-29T16:30:27.753099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-29T16:30:27.807197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01389
64.6%
1760
35.4%

Most occurring characters

ValueCountFrequency (%)
01389
64.6%
1760
35.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2149
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01389
64.6%
1760
35.4%

Most occurring scripts

ValueCountFrequency (%)
Common2149
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01389
64.6%
1760
35.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01389
64.6%
1760
35.4%

DoctorInCharge
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.9 KiB
XXXConfid
2149 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters19341
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowXXXConfid
2nd rowXXXConfid
3rd rowXXXConfid
4th rowXXXConfid
5th rowXXXConfid

Common Values

ValueCountFrequency (%)
XXXConfid2149
100.0%

Length

2025-11-29T16:30:27.870451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

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ValueCountFrequency (%)
xxxconfid2149
100.0%

Most occurring characters

ValueCountFrequency (%)
X6447
33.3%
C2149
 
11.1%
o2149
 
11.1%
n2149
 
11.1%
f2149
 
11.1%
i2149
 
11.1%
d2149
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10745
55.6%
Uppercase Letter8596
44.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o2149
20.0%
n2149
20.0%
f2149
20.0%
i2149
20.0%
d2149
20.0%
Uppercase Letter
ValueCountFrequency (%)
X6447
75.0%
C2149
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin19341
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
X6447
33.3%
C2149
 
11.1%
o2149
 
11.1%
n2149
 
11.1%
f2149
 
11.1%
i2149
 
11.1%
d2149
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII19341
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
X6447
33.3%
C2149
 
11.1%
o2149
 
11.1%
n2149
 
11.1%
f2149
 
11.1%
i2149
 
11.1%
d2149
 
11.1%

Interactions

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2025-11-29T16:30:13.818964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:15.356697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:17.278489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:18.622522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:19.902775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:21.233202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:01.498357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:03.038989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:04.364062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:05.756386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:07.327048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:08.641355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:09.978943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:11.269208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:10.957585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:12.455108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:13.928046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:15.460829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:17.367014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:18.718668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:19.992003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:21.309445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:01.586224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:03.124681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:04.445898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:05.869514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:07.414276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:08.734520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:10.064574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:11.354808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:11.044175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:12.557777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:14.048441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:15.565579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:17.454931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:18.804385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:20.079219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:21.379991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:01.680228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:03.197512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:04.521675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:05.977649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:07.494985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:08.820338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:10.151044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:11.429433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:11.126820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:12.678767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:14.153326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:15.665504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:17.533741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:18.883792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:20.168160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:21.451646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:01.912998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:03.273553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:04.597795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:06.072286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:07.577422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:08.897363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:10.230966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:11.501649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:11.211151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:12.779319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:14.244495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:15.780976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:17.617073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:18.954998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-29T16:30:20.248545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-29T16:30:28.001204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ADLAgeAlcoholConsumptionBMIBehavioralProblemsCardiovascularDiseaseCholesterolHDLCholesterolLDLCholesterolTotalCholesterolTriglyceridesConfusionDepressionDiabetesDiagnosisDiastolicBPDietQualityDifficultyCompletingTasksDisorientationEducationLevelEthnicityFamilyHistoryAlzheimersForgetfulnessFunctionalAssessmentGenderHeadInjuryHypertensionMMSEMemoryComplaintsPatientIDPersonalityChangesPhysicalActivitySleepQualitySmokingSystolicBP
ADL1.000-0.038-0.007-0.0100.0580.0000.006-0.0200.0000.0230.0000.0000.0390.372-0.005-0.0090.0000.0000.0360.0070.0350.0230.0530.0000.0000.0240.0030.000-0.0210.051-0.0140.0140.0000.015
Age-0.0381.0000.008-0.0160.0230.0000.0080.0040.000-0.0040.0000.0000.0000.000-0.004-0.0240.0380.0000.0000.0270.0000.0000.0050.0290.0000.000-0.0040.0000.0030.030-0.0110.0480.025-0.005
AlcoholConsumption-0.0070.0081.000-0.0090.0000.000-0.002-0.018-0.0340.0240.0220.0250.0000.043-0.0090.0200.0000.0400.0450.0270.0240.000-0.0160.0000.0000.042-0.0110.000-0.0200.0000.023-0.0040.000-0.030
BMI-0.010-0.016-0.0091.0000.0450.0000.0380.0240.002-0.0180.0000.0000.0000.034-0.0020.0200.0530.0450.0000.0060.0000.045-0.0310.0440.0000.000-0.0030.051-0.0340.0110.000-0.0060.028-0.019
BehavioralProblems0.0580.0230.0000.0451.0000.0000.0230.0170.0000.0000.0000.0000.0150.2220.0000.0380.0000.0000.0130.0000.0050.0040.0260.0000.0400.0210.0000.0000.0000.0000.0600.0000.0000.000
CardiovascularDisease0.0000.0000.0000.0000.0001.0000.0200.0000.0530.0210.0000.0000.0000.0210.0000.0000.0080.0090.0000.0150.0000.0000.0620.0250.0000.0000.0000.0200.0000.0220.0180.0180.0150.000
CholesterolHDL0.0060.008-0.0020.0380.0230.0201.000-0.0380.0090.0160.0460.0440.0330.0240.008-0.0090.0400.0090.0200.0000.0180.028-0.0030.0410.0150.034-0.0060.039-0.0120.000-0.0020.0140.0400.004
CholesterolLDL-0.0200.004-0.0180.0240.0170.000-0.0381.0000.010-0.0060.0000.0000.0400.000-0.016-0.0240.0000.0000.0000.0210.0360.039-0.0170.0420.0000.0000.0250.044-0.0240.0000.0180.0080.000-0.007
CholesterolTotal0.0000.000-0.0340.0020.0000.0530.0090.0101.000-0.0030.0470.0000.0550.0210.014-0.0160.0130.0210.0000.0000.0000.000-0.0070.0000.0000.000-0.0130.0420.0050.0430.0140.0070.0000.018
CholesterolTriglycerides0.023-0.0040.024-0.0180.0000.0210.016-0.006-0.0031.0000.0000.0760.0480.053-0.0080.0350.0000.0000.0000.0350.0380.000-0.0100.0000.0380.000-0.0080.0480.0110.0000.0270.0240.000-0.035
Confusion0.0000.0000.0220.0000.0000.0000.0460.0000.0470.0001.0000.0000.0000.0000.0000.0000.0000.0000.0430.0020.0000.0000.0000.0200.0200.0000.0000.0000.0000.0000.0000.0000.0000.036
Depression0.0000.0000.0250.0000.0000.0000.0440.0000.0000.0760.0001.0000.0000.0000.0000.0460.0000.0000.0430.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0110.0520.0000.0310.000
Diabetes0.0390.0000.0000.0000.0150.0000.0330.0400.0550.0480.0000.0001.0000.0210.0140.0000.0000.0000.0000.0000.0010.0000.0390.0000.0000.0000.0000.0000.0000.0000.0410.0370.0270.020
Diagnosis0.3720.0000.0430.0340.2220.0210.0240.0000.0210.0530.0000.0000.0211.0000.0000.0000.0000.0090.0260.0390.0230.0000.4090.0000.0000.0260.3150.3050.0980.0000.0000.0000.0000.000
DiastolicBP-0.005-0.004-0.009-0.0020.0000.0000.008-0.0160.014-0.0080.0000.0000.0140.0001.0000.0110.0210.0510.0000.0000.0520.0000.0310.0000.0000.064-0.0270.000-0.0070.000-0.0090.0090.0310.002
DietQuality-0.009-0.0240.0200.0200.0380.000-0.009-0.024-0.0160.0350.0000.0460.0000.0000.0111.0000.0420.0350.0370.0000.0000.000-0.0090.0000.0240.0270.0210.030-0.0180.0630.0110.0510.0320.006
DifficultyCompletingTasks0.0000.0380.0000.0530.0000.0080.0400.0000.0130.0000.0000.0000.0000.0000.0210.0421.0000.0000.0000.0620.0000.0000.0570.0000.0000.0000.0170.0370.0000.0290.0400.0490.0000.053
Disorientation0.0000.0000.0400.0450.0000.0090.0090.0000.0210.0000.0000.0000.0000.0090.0510.0350.0001.0000.0140.0000.0270.0220.0000.0000.0220.0230.0000.0000.0310.0000.0000.0000.0160.000
EducationLevel0.0360.0000.0450.0000.0130.0000.0200.0000.0000.0000.0430.0430.0000.0260.0000.0370.0000.0141.0000.0280.0110.0000.0290.0000.0000.0630.0060.0000.0590.0000.0000.0000.0000.000
Ethnicity0.0070.0270.0270.0060.0000.0150.0000.0210.0000.0350.0020.0000.0000.0390.0000.0000.0620.0000.0281.0000.0000.0060.0000.0000.0000.0000.0000.0110.0000.0370.0000.0000.0510.016
FamilyHistoryAlzheimers0.0350.0000.0240.0000.0050.0000.0180.0360.0000.0380.0000.0000.0010.0230.0520.0000.0000.0270.0110.0001.0000.0000.0000.0000.0000.0000.0520.0160.0000.0000.0000.0000.0390.020
Forgetfulness0.0230.0000.0000.0450.0040.0000.0280.0390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0060.0001.0000.0000.0170.0000.0190.0340.0000.0000.0000.0000.0000.0000.037
FunctionalAssessment0.0530.005-0.016-0.0310.0260.062-0.003-0.017-0.007-0.0100.0000.0000.0390.4090.031-0.0090.0570.0000.0290.0000.0000.0001.0000.0460.0000.0000.0230.0660.0250.000-0.0010.0300.0000.012
Gender0.0000.0290.0000.0440.0000.0250.0410.0420.0000.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0461.0000.0000.0000.0610.0000.0490.0120.0000.0000.0000.038
HeadInjury0.0000.0000.0000.0000.0400.0000.0150.0000.0000.0380.0200.0000.0000.0000.0000.0240.0000.0220.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0200.0000.0000.0000.000
Hypertension0.0240.0000.0420.0000.0210.0000.0340.0000.0000.0000.0000.0210.0000.0260.0640.0270.0000.0230.0630.0000.0000.0190.0000.0000.0001.0000.0800.0000.0000.0000.0000.0000.0000.000
MMSE0.003-0.004-0.011-0.0030.0000.000-0.0060.025-0.013-0.0080.0000.0000.0000.315-0.0270.0210.0170.0000.0060.0000.0520.0340.0230.0610.0000.0801.0000.000-0.0070.033-0.0090.0110.000-0.004
MemoryComplaints0.0000.0000.0000.0510.0000.0200.0390.0440.0420.0480.0000.0000.0000.3050.0000.0300.0370.0000.0000.0110.0160.0000.0660.0000.0000.0000.0001.0000.0000.0190.0000.0000.0000.000
PatientID-0.0210.003-0.020-0.0340.0000.000-0.012-0.0240.0050.0110.0000.0000.0000.098-0.007-0.0180.0000.0310.0590.0000.0000.0000.0250.0490.0000.000-0.0070.0001.0000.005-0.012-0.0270.036-0.021
PersonalityChanges0.0510.0300.0000.0110.0000.0220.0000.0000.0430.0000.0000.0110.0000.0000.0000.0630.0290.0000.0000.0370.0000.0000.0000.0120.0200.0000.0330.0190.0051.0000.0000.0310.0000.000
PhysicalActivity-0.014-0.0110.0230.0000.0600.018-0.0020.0180.0140.0270.0000.0520.0410.000-0.0090.0110.0400.0000.0000.0000.0000.000-0.0010.0000.0000.000-0.0090.000-0.0120.0001.000-0.0010.000-0.004
SleepQuality0.0140.048-0.004-0.0060.0000.0180.0140.0080.0070.0240.0000.0000.0370.0000.0090.0510.0490.0000.0000.0000.0000.0000.0300.0000.0000.0000.0110.000-0.0270.031-0.0011.0000.000-0.029
Smoking0.0000.0250.0000.0280.0000.0150.0400.0000.0000.0000.0000.0310.0270.0000.0310.0320.0000.0160.0000.0510.0390.0000.0000.0000.0000.0000.0000.0000.0360.0000.0000.0001.0000.000
SystolicBP0.015-0.005-0.030-0.0190.0000.0000.004-0.0070.018-0.0350.0360.0000.0200.0000.0020.0060.0530.0000.0000.0160.0200.0370.0120.0380.0000.000-0.0040.000-0.0210.000-0.004-0.0290.0001.000

Missing values

2025-11-29T16:30:21.812771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-29T16:30:22.025857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PatientIDAgeGenderEthnicityEducationLevelBMISmokingAlcoholConsumptionPhysicalActivityDietQualitySleepQualityFamilyHistoryAlzheimersCardiovascularDiseaseDiabetesDepressionHeadInjuryHypertensionSystolicBPDiastolicBPCholesterolTotalCholesterolLDLCholesterolHDLCholesterolTriglyceridesMMSEFunctionalAssessmentMemoryComplaintsBehavioralProblemsADLConfusionDisorientationPersonalityChangesDifficultyCompletingTasksForgetfulnessDiagnosisDoctorInCharge
047517300222.927749013.2972186.3271121.3472149.02567900110014272242.36684056.15089733.682563162.18914321.4635326.518877001.725883000100XXXConfid
147528900026.82768104.5425247.6198850.5187677.15129300000011564231.162595193.40799679.028477294.63090920.6132677.118696002.592424000010XXXConfid
247537303117.795882019.5550857.8449881.8263359.67357410000099116284.181858153.32276269.77229283.6383247.3562495.895077007.119548010100XXXConfid
347547410133.800817112.2092668.4280017.4356048.392554000000118115159.58224065.36663768.457491277.57735813.9911278.965106016.481226000000XXXConfid
447558900020.716974018.4543566.3104610.7954985.59723800000094117237.60218492.86970056.874305291.19878013.5176096.045039000.014691001100XXXConfid
547568611130.62688604.1401440.2110621.5849227.26195300100016862280.712539198.33462979.080503263.94365527.5175295.510144009.015686100000XXXConfid
647576803238.38762210.6460479.2576955.8973885.47768600001014388263.73414952.47067066.533369216.4891751.9644136.062124009.236328000010XXXConfid
747587500118.776009013.7238264.6494518.3419034.21321000000011763151.38313769.62351077.346816210.57086610.1395683.401374004.517248100011XXXConfid
847597211027.833188012.1678481.5313606.7368825.748224000001117119233.605755144.04574043.075893151.16418625.8207327.396061010.756232001000XXXConfid
947608700035.456302116.0286886.4407738.0860197.55177301000013078281.630050130.49758074.291247144.17597528.3884091.148904014.554394000000XXXConfid
PatientIDAgeGenderEthnicityEducationLevelBMISmokingAlcoholConsumptionPhysicalActivityDietQualitySleepQualityFamilyHistoryAlzheimersCardiovascularDiseaseDiabetesDepressionHeadInjuryHypertensionSystolicBPDiastolicBPCholesterolTotalCholesterolLDLCholesterolHDLCholesterolTriglyceridesMMSEFunctionalAssessmentMemoryComplaintsBehavioralProblemsADLConfusionDisorientationPersonalityChangesDifficultyCompletingTasksForgetfulnessDiagnosisDoctorInCharge
213968906801017.82896502.9827478.3948260.3175265.73678000011010261236.649402173.90269948.917216334.38573110.8282509.104117008.819115000100XXXConfid
214068918901234.42241907.7706870.9475675.7321394.91776001000114278227.11122858.54891028.587889187.7893394.9264001.605154008.734082010000XXXConfid
214168927200221.600144019.3917668.1814696.6401957.08809110000013791264.994941181.68352196.361322347.47961920.0137868.722739009.570776000010XXXConfid
214268938800020.09760004.0894587.3896332.8787386.271699000001166105190.975712104.92057336.59371181.07513025.1409037.729270006.156040000101XXXConfid
214368946612132.01380619.3087064.3524025.4323749.62431210000110199233.95410857.45417067.16267598.6880955.9036891.405821004.544538000111XXXConfid
214468956100139.12175701.5611264.0499646.5553067.535540000000122101280.47682494.87049060.943092234.5201231.2011900.238667004.492838100001XXXConfid
214568967500217.857903018.7672611.3606672.9046628.555256000000152106186.38443695.41070093.649735367.9868776.4580608.687480019.204952000001XXXConfid
214668977700115.47647904.5946709.8860028.1200255.769464000000115118237.024558156.26729499.678209294.80233817.0110031.972137005.036334000001XXXConfid
214768987813115.29991108.6745056.3542821.2634278.32287401000010396242.19719252.48296181.281111145.2537464.0304915.173891003.785399000011XXXConfid
214868997200233.28973807.8907036.5709937.9414049.87871100000016678283.39679792.20006481.920043217.39687311.1147776.307543018.327563010010XXXConfid